Test3_vgg_VGG网络的源码_VGG源码_源码

from tensorflow.keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt from model import vgg import tensorflow as tf import json import os data_root = os.path.abspath(os.path.join(os.getcwd(), "..")) # get data root path image_path = data_root + "/flower_data/" # flower data set path train_dir = image_path + "train" validation_dir = image_path + "val" # create direction for saving weights if not os.path.exists("save_weights"): os.makedirs("save_weights") im_height = 224 im_width = 224 batch_size = 32 epochs = 10 # data generator with data augmentation train_image_generator = ImageDataGenerator(rescale=1. / 255, horizontal_flip=True) validation_image_generator = ImageDataGenerator(rescale=1. / 255) train_data_gen = train_image_generator.flow_from_directory(directory=train_dir, batch_size=batch_size, shuffle=True, target_size=(im_height, im_width), class_mode='categorical') total_train = train_data_gen.n # get class dict class_indices = train_data_gen.class_indices # transform value and key of dict inverse_dict = dict((val, key) for key, val in class_indices.items()) # write dict into json file json_str = json.dumps(inverse_dict, indent=4) with open('class_indices.json', 'w') as json_file: json_file.write(json_str) val_data_gen = train_image_generator.flow_from_directory(directory=validation_dir, batch_size=batch_size, shuffle=True, target_size=(im_height, im_width), class_mode='categorical') total_val = val_data_gen.n model = vgg("vgg16", 224, 224, 5) model.summary() # using keras high level api for training model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False), metrics=["accuracy"]) callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath='./save_weights/myAlex_{epoch}.h5', save_best_only=True, save_weights_only=True, monitor='val_loss')] # tensorflow2.1 recommend to using fit history = model.fit(x=train_data_gen, steps_per_epoch=total_train // batch_size, epochs=epochs, validation_data=val_data_gen, validation_steps=total_val // batch_size, callbacks=callbacks)